The goal of this observational study is to evaluate the diagnostic accuracy of a CNN-based artificial intelligence model in patients with solitary skin lesions. The main questions it aims to answer are: * What is the diagnostic performance (sensitivity and specificity) of the CNN-based model in identifying solitary skin lesions using macroscopic clinical images? * How does the diagnostic accuracy of the CNN-based model compare with the evaluations performed by dermatologists and non-dermatologist physicians? Researchers will compare the AI model's diagnostic outputs to the independent evaluations of dermatologists and non-dermatologist physicians to see if the AI model can achieve a diagnostic performance comparable to or better than human clinicians. Participants (physicians acting as clinical readers) will: * Independently review a predefined set of anonymized macroscopic clinical images sourced from a retrospective patient archive. * Provide a primary diagnosis for each lesion based solely on the images, without access to patient history or histopathological results. * Submit their assessments to be compared against the gold standard (histopathological diagnosis) and the AI model's results.
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Diagnostic accuracy of the CNN-based artificial intelligence model
Timeframe: Baseline (Retrospective data analysis will be completed within 4 months)